You are here

Real time video monitoring of falls in memory care facilities for individuals with Alzheimer s and related dementias

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R43AG058354-01
Agency Tracking Number: R43AG058354
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NIA
Solicitation Number: PAS17-064
Timeline
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-09-15
Award End Date (Contract End Date): 2018-08-31
Small Business Information
2935 MLK JR WAY, UNIT C
Berkeley, CA 94703-2166
United States
DUNS: 080097739
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 GEORGE NETSCHER
 (713) 822-6924
 gnetscher@gmail.com
Business Contact
 GEORGE NETSCHER
Phone: (713) 822-6924
Email: gnetscher@gmail.com
Research Institution
N/A
Abstract

In the US Alzheimer s disease AD is the single most expensive disease the only disease in the
top six for which the number of deaths is increasing The greatest cost contributors are frequent
hospitalizations where falls are the largest culprit and frequent need for assistance with the
activities of daily living A fall safety system shows the potential to reduce costs and increase
quality of care by reducing the likelihood of emergency events e g detecting falls before a
fracture occurs and reducing the number of repeat falls Unfortunately current safety devices
require wearable or sensor technology not suitable for individuals with dementia and incapable of
showing caregivers how falls occur

Our goal is to deploy and demonstrate NestSense also known as SafelyYou an online fall detection
system with off the shelf wall mounted cameras to passively detect falls for patients with AD and
related dementias ADRD enabled by a human in the loop HIL The HIL will confirm the fall
detection alerts provided by our artificial intelligence algorithms We will demonstrate it for
patients in memory care facilities Memory care facilities can select parameters that matter for
specific patients for ex some patients wake up frequently during the night while others should
be assisted when they attempt to leave the bed at night It does not require action of individuals
caregivers such as wearing a fall pendant and is therefore well suited for individuals with ADRD
We leverage our HIL paradigm in which our deep learning a subfield of artificial intelligence
approaches identify and pre filter falls well enough to leave the last check to a human who will
call the facilities in case of detected safety critical events falls The human can monitor
several facilities at a given time

This project leverages the already recruited patients in our partner memory care facilities
recruited through our previous IRB approved pilot The work will leverage our previous three
pilots
Pilot We demonstrated the feasibility of the system by collecting a proof of concept data
containing acted falls of healthy subjects and showed accurate fall detection
Pilot We demonstrated acceptance of privacy safety tradeoffs by patients family and staff
through the collection of months of video data at WindChime of Marin a memory care facility from
the Integral Senior Living network in which we identified total hours of fall data This led to
clinical benefits including a reduction of falls from and in the first months to in the
final month due to video review with care staff
Pilot ongoing We demonstrated scalability and further acceptance by deploying the system in
facilities of the Carlton Integral Senior Living Pacifica and SRG networks totaling
patients already monitored by our system offline
The pilot proposed for this SBIR Phase I will translate the cameras in these facilities into a
real time fall detection system which will run online for months with a HIL support
Compared to a month baseline from the cameras recording with the detection offline we
hypothesize this real time detection system will lead to a statistically significant reduction in
time on the ground after a fall fall related hospitalizations and length of hospital stay
following fall incidents based on results described in previous clinical trials with
participants The proposed NestSense system uses off the shelf wall mounted cameras to perform detection of
safety critical events for Alzheimer s disease and related dementia patients in memory care
facilities It does not rely on active use from the patients or caregivers e g through wearing
any kind of device The NestSense technology provides the first robust non wearable fall detection
tuned specifically to the privacy security tradeoffs of dementia care It provides an answer to
today s technology gaps far from reliability by use of a Human in the Loop HIL paradigm to
enable a quick rollout of the technology Following the acceptance of privacy safety tradeoffs of
our system by patients family and memory care staff in the Carlton Integral Senior Living
Pacifica and SRG networks we have deployed the system in offline mode in facilities of the
network monitoring patients The research implementation and deployment work encompassed in
this SBIR Phase I will provide validation that the existing prototype can be rolled out to a proof
of concept operational in real time mode with HIL to reduce fall related hospitalizations and
time spent on the ground The pilot follows previous pilots the most recent of which deploys
cameras with these patients in offline mode to study the effect on the fall rate of
occupational therapist review of fall video in memory care This study will translate those cameras
into a real time fall online detection system which will run online for months with a HIL
support Compared to a month baseline from the cameras recording with the detection offline we
hypothesize this real time detection system will lead to a statistically significant reduction in
time on the ground after a fall fall related hospitalizations and length of hospital stay
following fall incidents based on previous clinical trials with participants

* Information listed above is at the time of submission. *

US Flag An Official Website of the United States Government